This disclosure relates generally to a computer-based, real-time adaptive system for fraud detection implementing automatic binning, feature selection, adaptive statistical models, and score blending.
Statistical models generally provide superior fraud detection as compared to (expert) rules systems. Traditional statistical models, in a simplified sense, extract patterns from historical data and use these patterns on future data to aide decisions. The premise behind an adaptive model for fraud detection is that changes in fraud patterns which a traditional static statistical model could not foresee are compensated for by the adaptive nature of the model. As such, it is intended for situations where traditional models might prove sub-optimal. Typical such situations are:                Little or no historic data is available.        The historic data is known not to or expected not to match production data well.        The historic data had only a subset of the features available that will be available in production.        The historic data used to build the traditional statistical model was based on pooled data contributions from several data contributors and the production data available after deployment is expected to not match the pooled data contributions.        